Various visual grounding techniques retroactively provide interpretability to computer vision models after they have been trained.
The first major line of works backpropagates the importance score through the layers of the neural network from the model's output to the individual pixels in the input, e.g. Excitation Backprop [1]} and Layer-wise Relevance Propagation [2]}.
Grad-CAM [3]}, a generalization of CAM [4]}, computes the regular gradients up to a selected intermediate layer and then combines them with the corresponding activations to get a low-resolution saliency map.
A disadvantage of these methods is that they are usually tailored to the model's architecture, i.e. they cannot be used for new network architectures without implementing new layers. However, these methods are very fast.
